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Memristor, with the ability of analog computing, is widely investigated for improving the computing efficiency of deep neural networks (DNNs) deployment. However, how to fully take advantage of the analog computing ability of memristive computing system (MCS) for DNN deployment is still an open question. Here, a new neural network models deployment scheme, that is, an information dimension matching (IDM) scheme, is proposed to fully take advantage of the analog computing ability of MCS. Furthermore, the spatial and temporal DNN, that is convolutional neural network (CNN) and recurrent neural network (RNN) is used to verify the proposed deployment scheme, respectively. The experimental results indicate that, compared to the traditional deployment schemes, the proposed deployment scheme shows obvious inference accuracy and energy efficiency improvement (>4 × in four‐layer DNNs deployment), and the energy efficiency improvement increases dramatically with the layers increment of DNNs. This work paves the path for developing high computing efficiency analog MCS. [ABSTRACT FROM AUTHOR]

A memristive neuron circuit which is capable of encoding input information via temporal and adaptive frequency methods within the same structure is proposed. Its multifunctionality allows switching between these modes by adjusting circuit capacitors. Furthermore, it is investigated how other parameters like input voltage and resistance influence output properties for different encoding methods. This research contributes to the development of more flexible neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

A memristor‐based reinforcement learning (RL) system has shown outstanding performance in achieving efficient autonomous decision‐making and edge computing. Sarsa (λ) is a classical multistep RL algorithm that records state with λ decay and guides policy updates, significantly improving the algorithm convergence speed. However, λ decay implementation of traditional computing hardware is confined by the extensive computation of power exponential decay. Herein, the value update equation for Sarsa (λ) is implemented by using the topological structure of the memristor array, without complex circuits. Where, most importantly, the critical λ decay function is realized by a TiOx‐based memristor with conductance decay property. The energy required for floating‐point operations can be significantly reduced while accelerating the convergence speed. Then, a path planning task is demonstrated based on intrinsic conductance decay property and shows outstanding performance. Finally, the information of rounds used for the task is obtained, which is based on the intrinsic decay property of the TiOx‐based memristor, maps into a 32 × 32 memristor array in parallel to calculate the value of each round. The results indicate that the experimental data have similar results to the simulations. Herein, thus, it provides a hardware‐enabled scheme for the memristor‐based RL algorithm implementation. [ABSTRACT FROM AUTHOR]

A more accurate synaptic modeling based on oxygen vacancy conductive mechanism is presented in this paper. Two internal state variables, that is, the length and area of conductive region, are used to describe the vertical and lateral growth/dissolution dynamics of the region, based on the physical mechanisms of ion drift and two different diffusion effects. Since the effect of length on the electric field is not negligible, it is introduced into the modeling. In addition, the Fick and Soret diffusions are considered, because they cause the model to produce a "forgetting" property and a "memory" retention. By the comparisons, this modeling captures the actual device better than others. In addition, the previous models can be derived from this one. Some rough analysis suggests that the modeling possibly captures different memristors by adjusting the parameters, and the effects of oxygen concentration and temperature on synapse can also be considered. Therefore, this modeling is more comprehensive and generalized. Several important synaptic functions are simulated, including excitatory postsynaptic current, paired‐pulse facilitation, spike‐rate‐dependent plasticity, and spike‐timing‐dependent plasticity, which may provide a theoretical basis for some potential applications such as artificial neural networks and artificial intelligence. [ABSTRACT FROM AUTHOR]

Herein, an improved memristor model with a controllable forgetting rate due to ion diffusion is proposed. A synaptic learning circuit based on this model is simulated with PSpice to complete synaptic bionics. First, the pulse pair superposition method, which is consistent with biological phenomena, is used to verify spike timing‐dependent plasticity (STDP); this proves that the modified model simulates biological synaptic behavior. Second, a habituation circuit constructed from a memristor and an analog behavioral modeling (ABM) device is designed to realize nonassociative learning. Finally, a new circuit with the improved model is built for Pavlov's associative memory implementation, simplifying the complexity of the circuit. The improved model can better realize the basic learning and memory modes of organisms and thus provide more brain‐inspired computing options. [ABSTRACT FROM AUTHOR]

Using hybrid density functional calculations, this paper systematically investigated the common defects in m-HfO2 and [ABSTRACT FROM AUTHOR]

In this paper, based on first-principles calculations, we have carried out a comprehensive study of substitutional oxygen defects in hexagonal silicon nitride (β-Si3N4). First of all, it is found that substitutional oxygens tend to form clusters at three different sites due to the intensive attractive interaction. By analyzing modified Bader charge and trap energy, we next discuss retention characteristic of the three clusters. The results manifest that all of the clusters are amphoteric defects with the ability to trap both electrons and holes. Moreover, every cluster is more powerful to trap holes, which indicates that oxygen clusters have a higher stability to hold holes than electrons. With regard to endurance characteristic, our studies reveal that the three clusters present differences after program/erase cycles, and then we explore the mechanism of endurance degeneration by nudged elastic band method (NEB). Taking full account of retention and endurance, we deem holes rather than electrons to be optimal to act as operational charge carriers for oxygen defects in Si3N4-based charge trapping memories. [ABSTRACT FROM AUTHOR]